The prevalence of cannabis use disorder in individuals with anxiety or related disorders: a systematic review
Bibliographic record
Abstract
The self-medication of underlying mental health symptoms is a primary reason for cannabis consumption, driving variation in the prevalence of Cannabis Use Disorder (CUD) in Anxiety and Related Disorders (ARDs). The current study aimed to systematically review predictors of CUD in individuals with a comorbid ARD diagnosis. An online search was conducted in January 2023 with a Boolean search phrase incorporating keywords related to CUD and ARDs in PubMed, PsycInfo, and WoS. Articles were included if participants/estimates were (a) at least 18 years of age; (b) prospectively assigned a diagnosis of current ARD supported by a clinician interview; c) diagnosed with current or lifetime CUD, cannabis dependence, or abuse via an interview or empirically validated screening tools; and (d) recruited from representative samples. A total of 1057 articles were screened. Five studies for the prevalence of CUD in ARDs (N = 10,896) met the inclusion criteria. Amongst these studies, the proportion of individuals with CUD in any ARD ranged from 3.3% to 19.8%. Amongst veterans with PTSD, four studies met inclusion criteria (N = 1,329), whereby the prevalence ranged from 4.2% to 34%. All results were synthesized narratively. There is a lack of research using clinician-administered interviews to identify accurate prevalence estimates in the literature, resulting in few studies. Studies failed to aggregate estimates of comorbid CUD in specific ARDs, making it difficult to ascertain whether different ARDs are at a higher risk of developing comorbid CUD. Despite methodological variation, this systematic review suggests that individuals with current ARDs may be at risk of developing comorbid CUD in their lifetime. However, future research should incorporate control groups and conduct cross-cultural studies to determine the extent of this relationship accurately.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".